Google [1]} coined the term FL in 2016, which is a privacy-preserving ML technique in which an ML model is collaboratively learned across several distributed devices (e.g., mobile phones), while all training data is kept on local devices. An FL system is shown in Figure REF . In the first step, the edge server initiates the global model and distributes the model parameters to all connected edge devices. In the second step, each local edge device trains a local version of the ML model on its local data. Instead of sending raw data to the server (edge), only the local model parameter updates are sent up to the server. In the third step, the server computes a weighted average of the parameter updates on the server using the Federated Averaging (FedAvg) [2]} algorithm to obtain the new set of parameters for the global model. In the fourth step, a new global model is then sent back down to each device for the next round of training by the edge server. The entire process is repeated until the model converges.
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